Journal Article10.1016/J.COMPAG.2019.01.012
Apple detection during different growth stages in orchards using the improved YOLO-V3 model
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TL;DR: The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model.
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About: This article is published in Computers and Electronics in Agriculture. The article was published on 01 Feb 2019.
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Citations
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TL;DR: In this article , the authors used two Logitech C920 webcam cameras mounted before and after the blower fan on a commercial mechanical wild blueberry harvester to develop a dataset of 1000 images.
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Performance Analysis of Path Planning Algorithms for Fruit Harvesting Robot
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TL;DR: The improved Rapidly exploring Random Tree algorithm outperformed all other algorithms under the given constraints and gave an optimal path as compared to the other algorithms due to its rewiring feature by an average of 21%.
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Deep-Learning Segmentation and Recognition of Tooth in Thresholded Panoramic X-ray
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TL;DR: In this article , a deep learning-based method was proposed to automatically examine the tooth and its condition using the Panoramic X-ray Image (PXI) using a series of operations such as image resizing, tooth region enhancement with thresholding, deep-learning-based segmentation, and tooth detection.
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New Progress in Intelligent Picking: Online Detection of Apple Maturity and Fruit Diameter Based on Machine Vision
Junsheng Liu,Guangze Zhao,Shuangxi Liu,Yi Liu,Huawei Yang,Jingwei Sun,Yinfa Yan,Guoqiang Fan,Jinxing Wang,Hongjian Zhang +9 more
TL;DR: Findings indicated that the proposed machine vision-based methodology for the accurate identification of Fuji apples’ maturity and diameter met the requirements of real-time mechanical harvesting operations, offering practical importance for the advancement of the apple industry.
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References
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 04 Sep 2014
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
102.6K
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
•Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
- 01 Jan 2015
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
51.9K
Densely Connected Convolutional Networks
Gao Huang,Zhuang Liu,Laurens van der Maaten,Kilian Q. Weinberger +3 more
- 21 Jul 2017
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon,Santosh K. Divvala,Ross Girshick,Ali Farhadi +3 more
- 27 Jun 2016
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.